Safe Data Analytics: Theory, Algorithms, and Applications

author: Xiaoli Li, Department of Electrical Engineering and Computer Science, University of Kansas
author: Chao Lan, Computer Science Department, University of Wyoming
author: Jun (Luke) Huan, Department of Electrical Engineering and Computer Science, University of Kansas
published: Nov. 21, 2017,   recorded: August 2017,   views: 876
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Description

Data science is penetrating virtually every aspect of our society. However, data science algorithms and systems, including data acquisition and processing pipelines and analytical techniques, are becoming increasingly complex. Many data science algorithms and systems are not transparent to the end-user. For example, how the underlying models work and when such models may fail, are not clear. Many approaches, especially those that apply to human subjects, may learn and reinforce pre-existing biases leading, for example, to unfair treatment of minority sections of a population. To enable the widespread adoption of data science approaches it is necessary to construct data analytics that operate safely and securely, in a controlled and transparent manner. However, current research in this area is very limited.

In this tutorial, we plan to cover three aspects of safe data analytics, namely, transparency, fairness and security. We present several real-world applications of safe data science to illustrate the importance of the topic. We review recent research efforts in data mining and machine learning to achieve safe data science based on different techniques and evaluation metrics. We conclude the tutorial by pointing out remaining challenges in current research and future directions.

Link to tutorial: http://www.ittc.ku.edu/~jhuan/kdd17T.html

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